OpenAI, proprietary

# o4-mini

> o4-mini by OpenAI, released April 2025. Ranked #132 of 354 with a Noometry Index of 41.6. API: $1.10 in / $4.40 out per M tokens. 200K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/o4-mini
- Last updated: 2026-10-10
- Title: o4-mini Benchmarks, Price & Rank (October 2026) | Noometry

o4-mini by OpenAI ranks 132nd of 354 ranked models on the Noometry Index as of October 2026, with a score of 41.6. Its strongest category is long context, where it ranks 33rd. API pricing starts at $1.10 per million input tokens and $4.40 per million output tokens, with a 200K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #132 of 354
- **Index score:** 41.6
- **Evidence:** Confirmed 60 results
- **Provider:** [OpenAI](https://noometry.com/providers/openai)
- **Released:** April 16, 2025
- **Weights:** Proprietary
- **Reasoning:** Yes
- **Context window:** 200K
- **Max output:** 100K
- **Input price:** $1.10 / M
- **Output price:** $4.40 / M
- **Blended price:** $1.93 / M
- **Output speed:** 6 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #150 of 219
- **Knowledge cutoff:** May 2024
- **Input:** text, image

## Category scores

Each category score combines every public result we have in that category.

o4-mini category scores

1.  Coding 40.9
2.  Agentic & Tool Use 32.6
3.  Reasoning 24.6
4.  Math 40.8
5.  Knowledge 43.6
6.  Multimodal 40.2
7.  Multilingual 47.0
8.  Instruction Following 75.2
9.  Long Context 45.5
10.  Writing & Preference 54.0
11.  020406080

o4-mini category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 40.9 | #127 | 6 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 32.6 | #61 | 2 |
| [Reasoning](https://noometry.com/best/reasoning) | 24.6 | #162 | 11 |
| [Math](https://noometry.com/best/math) | 40.8 | #89 | 6 |
| [Knowledge](https://noometry.com/best/knowledge) | 43.6 | #91 | 8 |
| [Multimodal](https://noometry.com/best/multimodal) | 40.2 | #49 | 3 |
| [Multilingual](https://noometry.com/best/multilingual) | 47.0 | #154 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 75.2 | #68 | 2 |
| [Long Context](https://noometry.com/best/long-context) | 45.5 | #33 | 2 |
| [Writing & Preference](https://noometry.com/best/writing) | 54.0 | #152 | 5 |

## Strengths and weaknesses

Categories where o4-mini places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

o4-mini: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Long Context](https://noometry.com/best/long-context) | 45.5 | +4.6 | #33 of 296, top 12% |
| [Instruction Following](https://noometry.com/best/instruction-following) | 75.2 | +3.9 | #68 of 305, top 23% |
| [Math](https://noometry.com/best/math) | 40.8 | +4.3 | #89 of 327, top 28% |

### Weakest categories

o4-mini: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Multilingual](https://noometry.com/best/multilingual) | 47.0 | −0.4 | #154 of 297, top 52% |
| [Writing & Preference](https://noometry.com/best/writing) | 54.0 | +0.2 | #152 of 312, top 49% |
| [Reasoning](https://noometry.com/best/reasoning) | 24.6 | +0.9 | #162 of 350, top 47% |

## Closest competitors

The models ranked just above and below o4-mini. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to o4-mini
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [GPT-5 Mini](https://noometry.com/models/gpt-5-mini) | #128 | 41.8 | $0.69 | 3 | [Compare](https://noometry.com/compare/gpt-5-mini-vs-o4-mini) |
| [ERNIE 5.0 0110](https://noometry.com/models/ernie-5-0) | #129 | 41.8 | — | — | [Compare](https://noometry.com/compare/ernie-5-0-vs-o4-mini) |
| [Granite 4.2 30b](https://noometry.com/models/granite-4-2-30b) | #130 | 41.8 | — | — | [Compare](https://noometry.com/compare/granite-4-2-30b-vs-o4-mini) |
| [Muse Glimmer](https://noometry.com/models/muse-glimmer) | #131 | 41.7 | — | — | [Compare](https://noometry.com/compare/muse-glimmer-vs-o4-mini) |
| [Gemini 3.5 Flash Lite](https://noometry.com/models/gemini-3-5-flash-lite) | #133 | 41.5 | $0.85 | — | [Compare](https://noometry.com/compare/gemini-3-5-flash-lite-vs-o4-mini) |
| [Grok 4.1](https://noometry.com/models/grok-4-1) | #134 | 41.5 | — | — | [Compare](https://noometry.com/compare/grok-4-1-vs-o4-mini) |
| [GLM-4.6](https://noometry.com/models/glm-4-6) | #135 | 41.4 | $1 | 12 | [Compare](https://noometry.com/compare/glm-4-6-vs-o4-mini) |
| [Grok 4.1 Fast](https://noometry.com/models/grok-4-1-fast) | #136 | 41.4 | $0.28 | — | [Compare](https://noometry.com/compare/grok-4-1-fast-vs-o4-mini) |

Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise)

## Benchmark results

Every published result we track, with its source. Bold rows are the ones used for ranking; where several exist we prefer independent runs over self-reported numbers.

### Coding

o4-mini Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SWE-bench Verified (bash only)](https://noometry.com/benchmarks/swe-bench-bash-only) | 45% | #29 of 39, top 75% |  | [SWE-bench](https://www.swebench.com/) | 2025-07-26 |
| [Aider Polyglot](https://noometry.com/benchmarks/aider-polyglot) | 72% | #8 of 44, top 19% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GSO](https://noometry.com/benchmarks/gso-bench) | 3.6% | #28 of 31, top 91% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 52.6% | #43 of 119, top 37% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1368 | #158 of 294, top 54% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [CadEval](https://noometry.com/benchmarks/cadeval) | 62% | #3 of 14, top 22% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 826.17 | #54 of 105, top 52% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [AlgoTune](https://noometry.com/benchmarks/algotune) | 1.72 | #6 of 18, top 34% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

o4-mini Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Berkeley Function Calling Leaderboard](https://noometry.com/benchmarks/bfcl) | 53.2% | #16 of 49, top 33% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| [GDPval](https://noometry.com/benchmarks/gdpval) | 25.3% | #8 of 11, top 73% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [METR Time Horizons](https://noometry.com/benchmarks/metr-time-horizons) | 63.9% | #16 of 32, top 50% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

o4-mini Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 6.1% | #52 of 83, top 63% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 1.7% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 2.4% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 38.7% | #55 of 77, top 72% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 67.6% | #28 of 99, top 29% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 58.7% | #52 of 83, top 63% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 21.3% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 41.8% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 0.6% | #88 of 134, top 66% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 26% | #41 of 129, top 32% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-12-08 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 14% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-11 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 20% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [EnigmaEval](https://noometry.com/benchmarks/enigmaeval) | 9.2% | #15 of 38, top 40% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [EnigmaEval](https://noometry.com/benchmarks/enigmaeval) | 6.8% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1351 | #159 of 297, top 54% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 5% | #71 of 74, top 96% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 77.6% | #80 of 151, top 53% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 26.5% | #83 of 125, top 67% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 145.64 | #80 of 213, top 38% |  | [Epoch AI](https://epoch.ai/eci) | 2025-04-16 |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 61.8 | #6 of 72, top 9% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

o4-mini Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 36.1% | #55 of 81, top 68% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-11 |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 16.1% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 28.8% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [FrontierMath Tier 4](https://noometry.com/benchmarks/frontiermath-tier-4) | 4.9% | #56 of 63, top 89% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-11 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 81.7% | #78 of 173, top 46% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-04-16 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 57.8% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-13 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 73.3% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [Omni-MATH](https://noometry.com/benchmarks/omni-math) | 72% | #2 of 57, top 4% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1389 | #139 of 285, top 49% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 97.8% | #3 of 79, top 4% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-04-16 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 24.8% | #25 of 68, top 37% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-13 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 10.7% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-16 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 19% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-13 |
| [FrontierMath Tier 4 (v1)](https://noometry.com/benchmarks/frontiermath-tier-4-v1) | 6.3% | #25 of 55, top 46% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-07-01 |
| [FrontierMath Tier 4 (v1)](https://noometry.com/benchmarks/frontiermath-tier-4-v1) | 2.1% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2025-08-07 |

### Knowledge

o4-mini Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 79.6% | #81 of 186, top 44% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2025-04-16 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 75.3% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-11 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 77.8% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [Humanity's Last Exam](https://noometry.com/benchmarks/hle) | 18.1% | #20 of 41, top 49% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Humanity's Last Exam](https://noometry.com/benchmarks/hle) | 14.3% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 18.8% |  | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 19.6% | #66 of 77, top 86% | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [MMLU-Pro](https://noometry.com/benchmarks/mmlu-pro) | 82% | #12 of 58, top 21% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [Confabulations](https://noometry.com/benchmarks/confabulations) (lower is better) | 15.8% | #22 of 51, top 44% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/confabulations) |  |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 18.6% | #89 of 96, top 93% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 18.6% | #89 of 96, top 93% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [GPQA (HELM)](https://noometry.com/benchmarks/helm-gpqa) | 73.5% | #6 of 57, top 11% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1343 | #157 of 273, top 58% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

o4-mini Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1194 | #82 of 122, top 68% |  | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |
| [GeoBench](https://noometry.com/benchmarks/geobench) | 64% | #15 of 25, top 60% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GeoBench](https://noometry.com/benchmarks/geobench) | 64% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [VPCT](https://noometry.com/benchmarks/vpct) | 57.5% | #6 of 24, top 25% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Multilingual

o4-mini Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1337 | #154 of 297, top 52% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1354 | #166 of 285, top 59% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1364 | #139 of 223, top 63% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1336 | #141 of 231, top 62% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1308 | #118 of 211, top 56% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1312 | #124 of 213, top 59% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1334 | #156 of 283, top 56% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1347 | #147 of 226, top 66% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

o4-mini Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [IFEval](https://noometry.com/benchmarks/ifeval) | 92.8% | #5 of 57, top 9% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1321 | #162 of 298, top 55% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

o4-mini Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Fiction.LiveBench](https://noometry.com/benchmarks/fiction-livebench) | 77.8% | #13 of 47, top 28% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1315 | #176 of 291, top 61% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

o4-mini Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1353 | #156 of 297, top 53% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1294 | #170 of 295, top 58% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Short-Story Creative Writing](https://noometry.com/benchmarks/lech-mazur-writing) | 75% | #25 of 39, top 65% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WildBench](https://noometry.com/benchmarks/wildbench) | 85.4% | #11 of 57, top 20% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1350 | #154 of 295, top 53% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

o4-mini API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [azure](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models) | $1.10 | $4.40 | $0.28 | 2026-10-10 |
| [openai](https://platform.openai.com/docs/models) | $1.10 | $4.40 | $0.28 | 2026-10-10 |
| [openrouter](https://openrouter.ai/openai/o4-mini) | $1.10 | $4.40 | $0.28 | 2026-10-10 |

[All OpenAI API prices →](https://noometry.com/llm-pricing/openai) [Estimate your cost →](https://noometry.com/tools/cost-calculator)

## Compare o4-mini

-   [o4-mini vs o3-mini](https://noometry.com/compare/o3-mini-vs-o4-mini)
-   [o4-mini vs Muse Glimmer](https://noometry.com/compare/muse-glimmer-vs-o4-mini)
-   [o4-mini vs Gemini 3.5 Flash Lite](https://noometry.com/compare/gemini-3-5-flash-lite-vs-o4-mini)
-   [o4-mini vs Granite 4.2 30b](https://noometry.com/compare/granite-4-2-30b-vs-o4-mini)
-   [o4-mini vs Grok 4.1](https://noometry.com/compare/grok-4-1-vs-o4-mini)
-   [o4-mini vs ERNIE 5.0 0110](https://noometry.com/compare/ernie-5-0-vs-o4-mini)
-   [o4-mini vs GLM-4.6](https://noometry.com/compare/glm-4-6-vs-o4-mini)
-   [o4-mini vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-o4-mini)
-   [o4-mini vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-o4-mini)
-   [o4-mini vs Kimi K3](https://noometry.com/compare/kimi-k3-vs-o4-mini)
-   [o4-mini vs Grok 4.6](https://noometry.com/compare/grok-4-6-vs-o4-mini)
-   [o4-mini vs Qwen3.8 Max](https://noometry.com/compare/o4-mini-vs-qwen3-8-max)
-   [o4-mini vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-o4-mini)
-   [o4-mini vs Muse Spark 1.3](https://noometry.com/compare/muse-spark-1-3-vs-o4-mini)

## Other OpenAI models

-   [GPT-6 Astra](https://noometry.com/models/gpt-6-astra)70.8
-   [GPT-6.1 Sol](https://noometry.com/models/gpt-6-1-sol)65.6
-   [GPT-5.6 Sol](https://noometry.com/models/gpt-5-6-sol)65.0
-   [GPT-5.5 Pro](https://noometry.com/models/gpt-5-5-pro)64.3
-   [GPT-5.5](https://noometry.com/models/gpt-5-5)63.4
-   [GPT-6 Sol](https://noometry.com/models/gpt-6-sol)61.8
-   [GPT-5.4](https://noometry.com/models/gpt-5-4)59.4
-   [GPT-5.6 Terra](https://noometry.com/models/gpt-5-6-terra)59.2

## Frequently asked questions

### How good is o4-mini?

o4-mini by OpenAI ranks 132nd of 354 ranked models on the Noometry Index as of October 2026, with a score of 41.6. Its strongest category is long context, where it ranks 33rd. API pricing starts at $1.10 per million input tokens and $4.40 per million output tokens, with a 200K-token context window.

### How much does o4-mini cost?

o4-mini costs $1.10 per million input tokens and $4.40 per million output tokens on OpenAI's own API, with cached input at $0.28.

### What is o4-mini's context window?

o4-mini accepts up to 200K tokens of input and can write up to 100K tokens in one response.

### Is o4-mini open source?

No. o4-mini is proprietary and available only through OpenAI's API and partner platforms.

### How fast is o4-mini?

o4-mini generated about 6 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.

### What are o4-mini's strengths and weaknesses?

Relative to other ranked models, o4-mini places best in long context, instruction following, math and lowest in multilingual, writing & preference, reasoning.

### What is o4-mini best at?

Its best category is long context, where it ranks 33rd on Noometry.

### Cite this page

Noometry. (2026). o4-mini benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/o4-mini

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/o4-mini.md).